@GLOSSDEF{bootword,
word="bootword",
definition="Throughout this work, a bootword is used to describe 
the word with which a noun co-occurs. The following \textbf{bootword types} are treated: verb 
(bootword)-noun (object), verb-noun (subject), adjective (bootword)-noun and various relations of the type: verb - noun(with specific theta-role)"
}


@GLOSSDEF{hypergraph,
word="hypergraph",
definition="A hypergraph is a graph whose edges can branch out and connect more than two nodes."} 

@GLOSSDEF{characteristic,
word="characteristic",
definition="The term characteristic refers to both features and ontological sorts."
}

@GLOSSDEF{precision,
word="precision",
definition="The ratio of found relevant (correct) tokens over the total number of found  tokes (correct and wrong). $ precision = \frac{number~ of ~relevant ~found ~tokens}{total ~number ~of ~found~tokens} $"}

@GLOSSDEF{recall,
word="recall",
definition="The ratio of found relevant tokens (correct ones) over the total number of tokens in the search domain.~~~~~~~~~~~ $ recall = \frac{number~ of ~relevant ~found ~tokens}{total ~number ~of ~tokens} $ "
}

@GLOSSDEF{f-score,
word="f-score",
definition="The f-score is a measure that combines precision and recall, 
it is the weighted harmonic mean of both. 
Depending of the parameter $\alpha$ one value can be weighted. 
With an $\alpha$ of $0.5$ precision is weighted twice as much as recall. 
The f-score is calculated with the following formula:$F_\alpha = \frac{(1 + \alpha) \times \mathrm{precision} \times \mathrm{recall}}{\alpha \times \mathrm{precision} + \mathrm{recall}}$ "
}

@GLOSSDEF{doubt,
word="doubt",
definition="A ratio for binary characteristics that reflects the doubt of classifications 
by comparing the results from different training sets. Calculated for each word by dividing the size of the smaller class by the size of the bigger class of one word."}


@GLOSSDEF{bias,
word="bias",
definition="Used for binary characteristics to show the ratio of the bigger class. Calculated by counting the number of occurrences of both classes and dividing the bigger number by the sum of both. Sometimes also used to show the ratio of the smaller class, though not in this work."}




@GLOSSDEF{verbalrelations,
word="verbal relations",
definition="This term refers to all types of relation, a verb can have with its complements or adjuncts. In this work these are subjects, objects and several theta-roles. "}

@GLOSSDEF{clustering,
word="clustering",
definition="Clustering is the action of grouping together objects, based on a specific similarity."}

@GLOSSDEF{seedwords,
word="seed words",
definition="In this work seed words are known nouns. These nouns and their corresponding features and ontological sorts are used during the learning phase, to calculate noun profiles for each bootword. With their help new nouns can be identified by the bootstrapping algorithm."}

@GLOSSDEF{context,
word="context",
definition="In this framework, context refers to the surroundings of a word in a sentence. These can be nearest neighbour words, such as the word left and right of the given word, or other important words of the sentence such as the main verb, if the given word stands in some relations to it."}


@GLOSSDEF{theta-role,
word="theta role",
definition="Theta roles are relations between a verb (or adjective or noun) and nouns in a given sentence. In this work, only theta-roles are used that are complements of the verb. Complements are nouns that fill a spot in the valency list of a verb. See sections \ref{section.theta.intro} for more details."}

@GLOSSDEF{valencylist,
word="valency list",
definition="Each verb in the lexicon has a valency list. In MultiNet there is a subcategorization frame which defines language dependent syntactic information for a verb. In addition there is also a language independent valency list. It defines in which theta roles (relations) a verb must stand with other elements of a sentence. These positions have to be filled by elements in order to complete the verb. However not necessarily in the syntactic surface structure of the sentence."}



@GLOSSDEF{bootstrapping,
word="bootstrapping",
definition="In this work bootstrapping is a method by which new information about unknown words can be obtained by means of already known seed words and similar contexts of both in a corpus."}

@GLOSSDEF{lexical-semantics,
word="lexical semantics",
definition="Lexical semantics is a subfield of semantic theory, which describes word meanings in their interrelations between with each other."
}

@GLOSSDEF{POS,
word="part-of-speech (POS)",
definition="The part-of-speech tag defines the category of a word, for example verb, noun, adjective"
}


@GLOSSDEF{corpus,
word="corpus",
definition="A collection of text, usually in machine-readable form and compiled to be representative of a particular 
kind of language and provided with some kind of annotation. 
For important corpus features see section \ref{subsection.corpus-features}."
}

@GLOSSDEF{collocation,
word="collocation",
definition="A sequence of words or terms which co-occur more often than would be expected by chance, such as \emph{make up}."}
}

@GLOSSDEF{co-occurrence,
word="co-occurrence",
definition="The above-chance frequent occurrence of two terms from a corpus next to each other in a specific order. Unlike collocation, co-occurrence assumes that the two terms are interdependent."
}
